# User inputs:
projDir         <- "/Users/amarsarvepalli/Desktop/github/Flagler"

# Airsage data files
airsage_data    <- c("airsage/trip_leg_matrix_cusWDH.csv",
                     "airsage/trip_leg_matrix_cusWDDP.csv",
                     "airsage/trip_leg_submatrix_cusWDDP.csv",
                     "airsage/trip_leg_submatrix_cusWDH.csv")
airsage_shapeFile <- "airsage/airsarge_districts/TAZs.shp"
airsage_Districts <- "airsage/airsarge_districts/district_equivalency.csv"

# Define corridor districts
Study_districts <- c(20:40)

# Census ACS data files
acs_shapeFile     <- "acs_data/Tract_2014_Pop_Emp.shp"

# Output files
daily_od_matrix <- "daily_od_trips_by_airsage_districts.csv"
peak_od_matrix <- "peak_od_trips_by_airsage_districts.csv"
daily_od_submatrix <- "daily_od_sub_trips_by_airsage_districts.csv"
peak_od_submatrix <- "peak_od_sub_trips_by_airsage_districts.csv"

1. Review of AirSage Data

This memo is divided into following sections:

  1. Airsage Data Files
  2. Review of Data: high level summaries
  3. Plot of OD zones and corridor
  4. Census socio-economic data
  5. Plot of current corridor bus routes

1.1 Airsage Data File Description

This is airsage data review. The following is the description of the data files

  1. trip_leg_matrix_cusWDH.csv and trip_leg_matrix_cusWDDP.csv: reporting trips between study area zones for average weekday 24 hour period and specific AM and PM day parts respectively.

  2. trip_leg_submatrix_cusWDH.csv and trip_leg_submatrix_cusWDDP.csv: reporting trips between study area zones that passed through the sub matrix analysis zone (in the SZCount column) for average weekday 24 hour period and specific AM and PM day parts respectively.

The following are the list of files received from Airsage along with: 1) number of samples 2) number of trips 3) ratio of trips to samples 4) percent of samples 5) percent of trips

## 
## 
## File                             Samples   Trips          Trips/Samples   % Sample   % Trips
## -------------------------------  --------  ------------  --------------  ---------  --------
## trip_leg_matrix_cusWDDP.csv      9666      2945178.48            30,469         38        19
## trip_leg_matrix_cusWDH.csv       6052      7457084.91           123,217         24        47
## trip_leg_submatrix_cusWDDP.csv   5143      1472307.68            28,627         20         9
## trip_leg_submatrix_cusWDH.csv    4301      4001471.97            93,036         17        25
## Total                            25162     15876043.04           63,095        100       100

Peak data flow shows a total observations of 9,666 whereas total daily flows are only 6,052. Does this mean the daily samples are only for offpeak? 46% of the daily flow are represented from 24% sample?

1.2 Contents of “trip_leg_matrix_cusWDDP.csv” and Contents of “trip_leg_matrix_cusWDDP.csv”

This file contains 9,666 observations and 9 fields. Each sample was geocoded with an Origin_Zone and Destination_Zone pairs and Count represents the total flows betweent these OD pairs. The start and end dates define the data collection period, which is April, 2015. The other fields: Subscriber_Class, Purpose and Time_of_Day describe the trip attributes.

  data_wddp <- read.csv("airsage/trip_leg_matrix_cusWDDP.csv")
  data_wdh <- read.csv("airsage/trip_leg_matrix_cusWDH.csv")
  data_wddp$tod <- "peak"
  data_wdh$tod <- "24H"
  data <- rbind(data_wddp, data_wdh)
  print(head(data))
##   Origin_Zone Destination_Zone Start_Date End_Date Aggregation
## 1          14               11   20150401 20150430          WD
## 2          11               34   20150401 20150430          WD
## 3          24               11   20150401 20150430          WD
## 4          10                2   20150401 20150430          WD
## 5          11               20   20150401 20150430          WD
## 6           2               17   20150401 20150430          WD
##   Subscriber_Class Purpose Time_of_Day   Count  tod
## 1         Resident     HBO     H07:H10 2273.11 peak
## 2         Resident     HBO     H07:H10  283.50 peak
## 3         Resident     NHB     H16:H19  336.66 peak
## 4         Resident     NHB     H16:H19 3715.68 peak
## 5         Resident     HBW     H07:H10 1644.29 peak
## 6          Visitor     NHB     H16:H19 1363.58 peak

Total trips from both files are shown below.

  # Total trips
  total_trips <- data %>% group_by(tod) %>% summarize(sum = sum(Count))
  kable(total_trips,format.args = list(big.mark = ","), digits = 0)
tod sum
24H 7,457,085
peak 2,945,178

The daily file contains more aggregated data with periods combined and thus the number of samples are fewer compared to AM/PM files. However, the total number of trips reveal that daily includes AM/PM trips although this needs to be confirmed from Airsage.

  # Break down by purpose and period
  samples <- data %>% group_by(tod, Purpose, Time_of_Day) %>% tally
  trips <- data %>% group_by(tod, Purpose, Time_of_Day) %>% summarize(sum = sum(Count))
  kable(left_join(samples,trips, by = c("Purpose" , "Time_of_Day", "tod")),format.args = list(big.mark = ","), digits = 0)
tod Purpose Time_of_Day n sum
24H HBO H00:H24 1,701 2,722,739
24H HBW H00:H24 1,385 1,267,599
24H NHB H00:H24 2,966 3,466,747
peak HBO H07:H10 1,332 482,475
peak HBO H16:H19 1,429 529,311
peak HBW H07:H10 1,137 363,070
peak HBW H16:H19 1,068 261,128
peak NHB H07:H10 2,286 545,530
peak NHB H16:H19 2,414 763,664

1.3 Contents of Daily file (trip_leg_matrix_cusWDH.csv)

Trips by purpose and resident type (Subscriber_Class)

  • There are a total of 7,457,084 OD flows in 24H.
  • About 83% of these trips (6.2 Million daily flows) are made by resident pouplation and the remaining 17% of trip are made by the visitors.
  • Most of the visitor trip purpose was Non-home based (NHB) as expected, however there are few handful of trips that are coded as Home-based other (HBO) trips (0.03%). These trips can be recoded into NHB as it makes less sense to assume a “home end” for visitors.
  • Of 6.2 Million resident trips, 17% are Home-based work (HBW), 29% are NHB and 36% are HBO trips. The work share of trips is in acceptable range for HBW trips (typically ~20%).
## [1] "Total trips = 7457084.91"
Subscriber_Class trips percent
Resident 6,201,677 83
Visitor 1,255,408 17
Subscriber_Class Purpose trips percent
Resident HBO 2,720,440 36
Resident HBW 1,267,599 17
Resident NHB 2,213,638 30
Visitor HBO 2,298 0
Visitor NHB 1,253,109 17

1.4 Contents of Peak period files (trip_leg_matrix_cusWDDP.csv)

Trips by purpose, resident type (Subscriber_Class) and time of day

  • There are a total of 2,945,178 OD flows in the peak two periods.
  • About 85% of these trips (2.5 Million flows) are made by resident pouplation and the remaining 15% of trip are made by the visitors.
  • Most of the visitor trip purpose was Non-home based (NHB) as expected, however there are few handful of trips that are coded as Home-based other (HBO) trips (0.02%). These trips can be recoded into NHB as it makes less sense to assume a “home end” for visitors.
  • Of 2.5 Million resident trips, 21% are Home-based work (HBW), 29% are NHB and 34% are HBO trips. The non-work share of trips seems too high given this file contains data only for the 6 hour peak periods.
  • Trips by time of day shows almost equal split between AM and PM peak periods with slightly more trips in PM period.
## [1] "Total trips = 2945178.48"
Subscriber_Class trips percent
Resident 2,508,516 85
Visitor 436,663 15
Subscriber_Class Purpose trips percent
Resident HBO 1,011,037 34
Resident HBW 624,198 21
Resident NHB 873,281 30
Visitor HBO 750 0
Visitor NHB 435,913 15
Time_of_Day trips percent
H07:H10 1,391,075 47
H16:H19 1,554,103 53

1.5 Airsage districts

The airsage data was provided for about 40 districts as shown in the below interactive map.

# Add airsage district file
shape <- readOGR(airsage_shapeFile, layer = "TAZs", verbose = FALSE)

# Read District equivqlency file
taz_dist <- read.csv(airsage_Districts)

# Append districts to 40 Zones
shape@data <- left_join(shape@data, taz_dist, by = "TAZ")

1.6 Study Area Flows

Districts 20 - 40 represent Flagler Corridor study area. The entire study area was defined as one super district “StudyArea” and the rest of the districts as “Outside” super district. The OD matrix generated for this super districts list the External-External (EE), Internal_External (IE), External_Internal (EI) and Internal-Internal (II) trips to the study area. The study area serves about 11% of the daily trips in the Miami-Dade county (sum of IE, EI and II trips).

Daily Study Area Flows

Tables below show the daily OD flows.

Outside StudyArea total
Outside 6,079,863 575,832 6,655,695
StudyArea 577,503 223,886 801,390
total 6,657,367 799,718 7,457,085
Outside StudyArea total
Outside 81.53 7.72 89.25
StudyArea 7.74 3.00 10.75
total 89.28 10.72 100.00
Origins Origin_Pct Destination Dest_Pct
1 7,155.59 0.89 8,255.22 1.03
2 74,591.54 9.31 65,398.40 8.18
3 4,332.24 0.54 3,457.41 0.43
4 6,055.95 0.76 5,162.41 0.65
5 3,464.71 0.43 3,207.18 0.40
6 12,026.33 1.50 11,644.73 1.46
7 13,688.95 1.71 13,006.25 1.63
8 6,120.03 0.76 5,877.74 0.73
9 26,166.67 3.27 25,915.70 3.24
10 50,962.80 6.36 48,879.78 6.11
11 70,945.94 8.85 70,634.60 8.83
12 20,345.92 2.54 21,342.21 2.67
13 31,732.68 3.96 32,990.42 4.13
14 76,594.30 9.56 87,669.36 10.96
15 46,132.33 5.76 44,039.42 5.51
16 41,626.60 5.19 41,924.58 5.24
17 79,137.40 9.88 79,362.06 9.92
18 2,100.69 0.26 2,266.80 0.28
19 4,322.68 0.54 4,797.60 0.60
20 223,886.22 27.94 223,886.22 28.00
total 801,389.57 100.00 799,718.09 100.00

Plot of total daily Origins and Destinations to/from Study Area.

Peak Study Area Flows

The following are the tables showing the peak trips

Outside StudyArea total
Outside 2,394,496 236,047 2,630,544
StudyArea 225,789 88,845 314,635
total 2,620,286 324,893 2,945,178
Outside StudyArea total
Outside 81.30 8.01 89.32
StudyArea 7.67 3.02 10.68
total 88.97 11.03 100.00 
## [1] "Percent of Peak Trips"
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 total
1 0.12 0.06 0.00 0.00 0.00 0.00 0.01 0.01 0.04 0.04 0.03 0.01 0.02 0.02 0.01 0.01 0.10 0.00 0.01 0.11 0.62
2 0.05 2.50 0.02 0.02 0.02 0.04 0.04 0.02 0.12 1.14 0.36 0.09 0.34 0.21 0.08 0.07 0.24 0.12 0.12 0.88 6.46
3 0.00 0.02 0.56 0.27 0.21 0.09 0.08 0.05 0.03 0.01 0.04 0.00 0.01 0.05 0.05 0.06 0.04 0.00 0.00 0.05 1.63
4 0.00 0.02 0.26 0.83 0.33 0.20 0.14 0.12 0.04 0.02 0.05 0.01 0.01 0.08 0.09 0.07 0.07 0.00 0.00 0.07 2.41
5 0.00 0.02 0.20 0.32 1.25 0.16 0.07 0.06 0.03 0.01 0.05 0.01 0.01 0.06 0.10 0.06 0.04 0.00 0.00 0.04 2.49
6 0.00 0.04 0.10 0.20 0.16 1.36 0.32 0.19 0.11 0.04 0.10 0.02 0.04 0.15 0.52 0.26 0.16 0.00 0.01 0.16 3.94
7 0.01 0.04 0.07 0.14 0.07 0.34 0.91 0.22 0.25 0.04 0.11 0.02 0.04 0.13 0.32 0.36 0.28 0.00 0.01 0.17 3.52
8 0.01 0.02 0.06 0.12 0.06 0.19 0.20 0.53 0.14 0.01 0.04 0.01 0.02 0.06 0.09 0.10 0.11 0.00 0.01 0.08 1.87
9 0.05 0.12 0.03 0.05 0.04 0.12 0.26 0.15 0.95 0.11 0.12 0.03 0.09 0.10 0.15 0.21 0.58 0.01 0.02 0.35 3.52
10 0.04 1.18 0.01 0.02 0.02 0.04 0.04 0.02 0.11 4.99 1.44 0.22 0.45 0.25 0.08 0.09 0.25 0.28 0.10 0.66 10.28
11 0.04 0.39 0.05 0.05 0.05 0.10 0.10 0.05 0.13 1.45 9.48 0.77 0.39 1.31 0.28 0.29 0.41 0.05 0.07 0.95 16.40
12 0.01 0.08 0.00 0.01 0.01 0.02 0.02 0.01 0.03 0.22 0.77 0.54 0.14 0.20 0.04 0.05 0.10 0.00 0.02 0.29 2.56
13 0.02 0.36 0.01 0.01 0.02 0.04 0.05 0.02 0.10 0.47 0.41 0.14 0.50 0.16 0.09 0.08 0.25 0.01 0.02 0.44 3.19
14 0.02 0.28 0.06 0.09 0.07 0.17 0.14 0.07 0.11 0.26 1.35 0.20 0.17 2.35 0.66 0.47 0.39 0.01 0.02 1.18 8.08
15 0.01 0.08 0.05 0.09 0.09 0.55 0.30 0.09 0.14 0.07 0.27 0.04 0.08 0.63 3.13 0.91 0.43 0.00 0.02 0.59 7.58
16 0.01 0.08 0.06 0.08 0.05 0.27 0.34 0.11 0.21 0.09 0.29 0.05 0.08 0.45 0.98 1.47 0.60 0.00 0.01 0.56 5.79
17 0.09 0.25 0.04 0.07 0.04 0.16 0.29 0.12 0.59 0.25 0.43 0.10 0.24 0.37 0.47 0.62 2.31 0.01 0.04 1.06 7.54
18 0.00 0.16 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.26 0.05 0.01 0.01 0.01 0.00 0.00 0.01 0.20 0.00 0.03 0.76
19 0.00 0.11 0.00 0.00 0.00 0.01 0.01 0.00 0.02 0.10 0.07 0.02 0.02 0.02 0.02 0.01 0.04 0.00 0.09 0.06 0.62
20 0.10 1.00 0.06 0.08 0.05 0.16 0.18 0.08 0.35 0.68 0.95 0.27 0.43 1.03 0.62 0.56 1.06 0.03 0.06 3.00 10.75
total 0.60 6.81 1.62 2.46 2.53 4.01 3.50 1.92 3.50 10.29 16.40 2.56 3.10 7.63 7.76 5.75 7.45 0.74 0.63 10.72 100.00

Inorder to better understand the travel patterns through the corridor, OD tables are developed to & from the studyarea super district to all other districts, where district 20 is the aggregated study area district.

1.7 Contents of Peak subarea files (trip_leg_submatrix_cusWDDP.csv)

Trips by purpose, resident type (Subscriber_Class) and time of day

  • There are a total of 1,472,307 OD flows in this file which represent EE flows through the region (Not sure I fully flow this file - Need some explaination from Airsage)
  • About 83% of these trips (1.2 Million flows) are made by resident pouplation and the remaining 17% of trip are made by the visitors.
  • 10% of these trips are External-External through trips (ee). This is computed based on SZCount field in the data.
  • The share of ee trips by purpose is consistent across all resident purposes and time of day.
  • Most of the visitor trip purpose was Non-home based (NHB) as expected.
## [1] "Total trips = 1472307.68"
Subscriber_Class trips ee pct_trips pct_ee ee_share
Resident 1229302.0 127292.81 83.49491 83.05297 10.35489
Visitor 243005.7 25974.21 16.50509 16.94703 10.68872
Subscriber_Class Purpose trips ee pct_trips pct_ee ee_share
Resident HBO 379186.23 40185.010 25.7545508 26.2189545 10.597698
Resident HBW 370170.93 30528.632 25.1422264 19.9185915 8.247172
Resident NHB 479944.84 56579.171 32.5981347 36.9154243 11.788682
Visitor HBO 51.36 41.595 0.0034884 0.0271389 80.987150
Visitor NHB 242954.32 25932.612 16.5015997 16.9198907 10.673863
Time_of_Day trips ee pct_trips pct_ee ee_share
H07:H10 674933.9 65511.15 45.8419 42.74315 9.706307
H16:H19 797373.8 87755.87 54.1581 57.25685 11.005612

1.8 Contents of Peak subarea files (trip_leg_submatrix_cusWDH.csv)

Trips by purpose, and resident type (Subscriber_Class)

  • There are a total of 4,001,471 OD flows in this file which represent EE flows through the region (Not sure I fully flow this file - Need some explaination from Airsage)
  • About 80% of these trips (2.9 Million flows) are made by resident pouplation and the remaining 20% of trip are made by the visitors.
  • About 9% of these trips are External-External through trips (ee). This is computed based on SZCount field in the data.
  • The share of ee trips by purpose is consistent across all resident purposes and time of day.
  • Most of the visitor trip purpose was Non-home based (NHB) as expected.
## [1] "Total trips = 4001471.97"
Subscriber_Class trips ee pct_trips pct_ee ee_share
Resident 3217870.5 298634.30 80.41717 80.1565 9.280495
Visitor 783601.5 73929.74 19.58283 19.8435 9.434609
Subscriber_Class Purpose trips ee pct_trips pct_ee ee_share
Resident HBO 1154883.54 105315.88 28.8614677 28.2678597 9.119177
Resident HBW 816881.18 52825.53 20.4145171 14.1789127 6.466734
Resident NHB 1246105.76 140492.89 31.1411843 37.7097290 11.274556
Visitor HBO 150.76 58.13 0.0037676 0.0156027 38.557973
Visitor NHB 783450.73 73871.61 19.5790633 19.8278959 9.429005
Time_of_Day trips ee pct_trips pct_ee ee_share
H00:H24 4001472 372564 100 100 9.310675

2. Review of Airsage data

Add Airsage data to the map and compute: 1. Samples origins and destinations by distrcits 2. Trips origins and destinations by area 3. Trips by prupose (table) 4. Trip flows by purpose (table, d3chord, flowmap)

3. Review of Census data

Review of Household, Population, Worker and Employment data

The map below displays population, and household and employment densities.

The map below displays employment and worker densities. THe worker densities are the household workers which is different the employment data.

3. Read Miami Dade Zones

3. Read Census ACS Data for 2014

# Get layer name from shape file
layerName <- strsplit(strsplit(acs_shapeFile,"[.]")[[1]],"/")[[1]][2]
# Add census shape files
acsShape <- readOGR(paste0(projDir,"/",acs_shapeFile), layer = layerName, verbose = FALSE)

map <- leaflet() %>%
  # Base Map
  addProviderTiles("Stamen.Toner",group = "Stamen") %>%
  addTiles(group = "OSM") %>%
  setView(lng = -80.1918, lat = 25.7617, zoom = 10) %>%
  addTiles %>%
  addPolygons(data = acsShape, group = "acs_data")
    
map